Vehicles configured to run on volatile fuel are required by state and federal regulations to be capable of determining the presence of evaporative leaks of a certain size, such as 0.01″. One common method for determining leaks relies on naturally occurring vacuum that builds in a sealed fuel tank following an engine off event (Engine-off natural vacuum, or EONV).
EONV tests are based on the relationship between gas temperature and pressure put forth in the ideal gas law. In a vehicle that has recently been turned off, the temperature of the fuel tank may decrease. The temperature decrease will cause fuel vapor to condense to liquid, creating a vacuum. A subsequent increase in temperature (e.g. during a diurnal cycle) will cause liquid fuel to volatize, increasing the pressure within a sealed tank. EONV tests typically involve predicting a fuel tank pressure that should be incurred due to a change in temperature over time. If the actual fuel tank pressure does not correspond to the predicted pressure, a leak may be indicated.
However, the relationship between fuel tank temperature and fuel tank pressure may be based on additional factors that may make fuel tank pressure predictions inaccurate. For example, the fuel volatility (or Reid Vapor Pressure) directly impacts the fuel vapor temperature/pressure relationship. Often, the actual fuel volatility will not be known. In another example, fuel sloshing prior to beginning the leak test may artificially increase the fuel tank pressure. Attempting to predict a fuel tank pressure when all contributing factors are not known may lead to a testing regimen that is neither accurate nor robust, which in turn may produce false results.
The inventors herein have recognized the above problems, and have developed systems and methods to at least partially address them. In one example, a method for an engine, comprising: sealing a fuel tank; and indicating fuel system degradation based on a comparison of a first correlation coefficient, determined based on a change in fuel tank pressure and a change in fuel tank temperature, to a second correlation coefficient determined based on a change in ambient pressure and a change in ambient temperature. In this way, leak tests, such as engine-off natural vacuum tests, may be performed without knowledge of the properties of the fuel stored in the fuel tank. In trying to predict an in-tank pressure change based on an in-tank temperature change, the volatility (Reid Vapor Pressure) of the fuel has an effect on the final pressure, and assuming or mis-estimating the volatility may lead to an incorrect analysis. However, the correlation between pressure and temperature may be predicted independently from the fuel properties.
In another example, a method for testing a vehicle fuel tank for leaks, comprising: sealing the vehicle fuel tank; determining whether a fuel tank fill level is between a first fill level limit and a second fill level limit; monitoring fuel tank temperature, fuel tank pressure, ambient temperature, and ambient pressure for a duration; determining entropy levels for fuel tank temperature, fuel tank pressure, ambient temperature, and ambient pressure over the duration; determining recursive means and standard deviations for fuel tank temperature, fuel tank pressure, ambient temperature, and ambient pressure over the duration; determining a first correlation coefficient based on the recursive means and standard deviations for fuel tank pressure and fuel tank temperature over the duration; determining a second correlation coefficient based on the recursive means and standard deviations for ambient pressure and ambient temperature over the duration; and indicating a fuel tank leak if the first correlation coefficient is less than the second correlation coefficient. In this way, an accurate depiction of the temperature/pressure relationship within a fuel tank may be obtained. By using recursive estimates (rather than simpler regression models), the temperature/pressure relationship may incorporate the vaporization of fuel with increases in temperature, and the condensation of fuel vapor with decreases in temperature. The correlation between temperature and pressure may then be used to accurately and robustly determine the integrity of the fuel tank.
In yet another example, a system for a vehicle, comprising: a fuel tank temperature sensor coupled to a fuel tank; a fuel tank pressure sensor coupled to the fuel tank; a fuel level sensor coupled within the fuel tank; an ambient temperature sensor; an ambient pressure sensor; one or more valves configured to seal the fuel tank when closed; and a controller configured with instructions stored in non-transitory memory, that when executed cause the controller to: seal the fuel tank; determine whether a fuel tank fill level is between a first fill level limit and a second fill level limit; monitor fuel tank temperature, fuel tank pressure, ambient temperature, and ambient pressure for a duration; determine entropy levels for fuel tank temperature, fuel tank pressure, ambient temperature, and ambient pressure over the duration; determine recursive means and standard deviations for fuel tank temperature, fuel tank pressure, ambient temperature, and ambient pressure over the duration; determine a first correlation coefficient based on the recursive means and standard deviations for fuel tank pressure and fuel tank temperature over the duration; determine a second correlation coefficient based on the recursive means and standard deviations for ambient pressure and ambient temperature over the duration; and indicate a fuel tank leak if the first correlation coefficient is less than the second correlation coefficient. The system may further comprise: a communications module configured to communicate with a cloud computing system; and the controller may be further configured with instructions stored in non-transitory memory, that when executed cause the controller to: retrieve one or more correlation models from the cloud computing system via the communications module; determine the first correlation coefficient based on the one or more correlation models; and determine the second correlation coefficient based on the one or more correlation models. In this way, leak test results and vehicle conditions from a plurality of vehicles may be aggregated at a cloud computing device and the aggregated data used to develop and refine correlation models, thresholds, and parameters. This crowd sourcing of data may allow for broad coverage of usage patterns and other impact factors to be incorporated into the parameters for a vehicle leak test, even if the vehicle itself has not previously encountered these impact factors. This, in turn, may lead to more accurate testing, and fewer testing failures.
The above advantages and other advantages, and features of the present description will be readily apparent from the following Detailed Description when taken alone or in connection with the accompanying drawings.
It should be understood that the summary above is provided to introduce in simplified form a selection of concepts that are further described in the detailed description. It is not meant to identify key or essential features of the claimed subject matter, the scope of which is defined uniquely by the claims that follow the detailed description. Furthermore, the claimed subject matter is not limited to implementations that solve any disadvantages noted above or in any part of this disclosure.